Today's organizations face continuous and unprecedented changes in their business environment. Traditional process design tools tend to be inflexible and can only support rigidly defined processes (e.g., order processing in the supply chain). This considerably restricts their real-world applications value, especially in the dynamic and changing environment. Recently, studies have contributed to the development of Adaptive Process Management Systems (APMS) that can facilitate fast implementation and deployment of business processes that allow for flexible adaptation. Most of them, however, only support ad hoc changes that require human intervention and only ensure structural correctness of these changes. This type of adaptation is time and effort consuming if such changes happen frequently. It is also difficult to trace and manage the sources of such changes so as to support automatic adaptation and improve reusability on the basis of past experiences. Furthermore, these approaches are not feasible in a knowledge-intensive environment because few of them examine semantic correctness of process changes. Hence, there is a need for new approaches that aim to design processes for flexibility and adaptability. This dissertation introduces a new approach toward integrating context-awareness in process flexibility and adaptation to overcome these limitations. This dissertation centers around three main projects using principles of the "design science methodology." First, we discuss the need of context-awareness in flexible process design and develop a formal approach to enable this design process. We propose to use an ontology-based method to model process contexts and Complex Event Processing (CEP) to detect critical situations. We also discuss the architectural support for context management. Next, we propose various adaptation strategies and integrate them at both process model and instance levels in a context-aware manner. Specifically, we developed a process template and rule-based approach, considering business contexts such organizational policies, to configure process models at design time. This can handle a larger number of process models with small variance and facilitate their management when business objectives change. At the instance level, we propose a placeholder-based approach to customize the subsequent workflow on the fly based on the dynamic contexts and case data at runtime. Since the context that impacts process instance adaptation is highly domain dependent, we describe this work in clinical settings in this dissertation. We proposed a framework called ConFlexFlow, and showed how flexible and adaptable clinical pathways can be designed taking into account medical knowledge in the form of rules and detailed contextual information to achieve a high quality outcome. These pathways are selected during workflow execution based on rules that encapsulate medical knowledge and the dynamic context at runtime. Thus, each process instance is customized to an individual patient case based on the patient data, resources availability, etc. Third, we propose a Mixed Integer Programming (MIP) model to check the compliance of process models and to validate the semantic correctness of process adaptations. We propose a formal specification language to model semantic constraints of activities, including presence and dependency relationships, ordering sequences, role assignment and obligations. Then the compliance issue is formulated as an MIP problem. We propose three novel ideas: the notion of a "degree of compliance" of a process, the concepts of "full" and "partial" validity of change operations, and the idea of "compliance by compensation." Based on the above ideas, we use mixed-integer programming to check: (a) the semantic compliance of a process model and its evolution; and (b) if the ad hoc changes made to running process instances are semantically valid or not. If not, we calculate the "minimal" set of compensation operations needed based on "degree of non-compliance," and transform a non-compliant process into a compliant one. We show that this novel approach is more elegant and superior to a pure logic-based approach. Throughout this dissertation, most of the examples are from the healthcare domain for illustration. Clinical workflow has been considered as a killing area for the application of process management technologies, due to its dynamic, complex and knowledge-intensive nature. We prove that our approach can improve the care quality by increasing the flexibility and adaptability of clinical pathways while ensuring its semantic correctness. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]